Identification and Estimation of Industry Dynamic Models with Persistent and Hidden State Variables

具有持久和隐藏状态变量的行业动态模型的识别和估计

• 批准号: 0137048
• 负责人: Jeffrey Campbell
• 金额: $ 11.82万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0137048&HistoricalAwards=false
• 关键词: Identification; Estimation; Industry; Dynamic; Models

This research project focuses on the formulation and estimation of structural models of producer entry, growth, and exit. The data source is new; a file of alcohol tax returns that contains the date of birth, date of exit, and a complete monthly history of the dollar value of alcohol sales for all licensed restaurants and bars in Texas. In the model, a firm's sales is proportional to its profits, but transitory cost shocks make it an imperfect indicator of profitability's persistent component. Because of this imperfect observation, the model's state variable is both persistent and hidden. The state variable's persistence distinguishes this model from the many estimable models of dynamic discrete choice that incorporate Rust's (1987) conditional independence assumption. To disentangle the persistent and transitory components of profitability, this project uses the fact that producers' exit decisions depend only on the persistent component. In the model with normally distributed shocks, the information in producers' exit decisions identifies the parameters describing both the persistent and transitory components of profitability as well as the producer's optimal exit threshold. An important component of this research project is the extension of this identification proof to semi-parametric and non parametric environments. The project's initial empirical research focuses on distinguishing Gaussian models of entrepreneurial learning similar to Jovanovic's (1982) from models with perfect entrepreneurial information, such as Hopenhayn's (1992). Additional information in the data set describing each restaurant or bar's location and the characteristics of its parent firm suggest further generalizations of the model and estimation technique.

本研究项目的重点是生产者进入，增长和退出的结构模型的制定和估计。 数据源是新的;一份酒精税申报表，其中包含出生日期、出境日期以及德克萨斯州所有有执照的餐馆和酒吧的酒精销售美元价值的完整月度历史记录。在该模型中，一个公司的销售额与其利润成正比，但短暂的成本冲击使其成为盈利能力的持久性组成部分的一个不完美的指标。由于这种不完美的观察，模型的状态变量是持久的和隐藏的。状态变量的持久性使这个模型区别于许多包含Rust（1987）条件独立假设的动态离散选择的可估计模型。为了区分盈利能力的持久性和暂时性成分，本项目使用了这样一个事实，即生产者的退出决定只取决于持久性成分。在具有正态分布冲击的模型中，生产商退出决策中的信息确定了描述盈利能力的持久性和暂时性成分以及生产商最佳退出阈值的参数。这个研究项目的一个重要组成部分是这个识别证明半参数和非参数环境的扩展。该项目最初的实证研究重点是区分类似于Jovanovic（1982）的创业学习高斯模型和具有完美创业信息的模型，如Hopenhayn（1992）。描述每个餐厅或酒吧的位置及其母公司的特征的数据集中的额外信息表明模型和估计技术的进一步概括。